{"id":"https://openalex.org/W4213117439","doi":"https://doi.org/10.1080/03610918.2022.2039199","title":"Analysis for partially accelerated dependent competing risks model with masked data based on copula function","display_name":"Analysis for partially accelerated dependent competing risks model with masked data based on copula function","publication_year":2022,"publication_date":"2022-02-15","ids":{"openalex":"https://openalex.org/W4213117439","doi":"https://doi.org/10.1080/03610918.2022.2039199"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2022.2039199","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2039199","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084206886","display_name":"Yue Li","orcid":"https://orcid.org/0000-0003-3758-4591"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yue Li","raw_affiliation_strings":["School of Mathematics and Statistics, Xidian University, Xi\u2019an, PR China","School of Mathematics and Statistics, Xidian University, Xi'an, PR China"],"raw_orcid":"https://orcid.org/0000-0003-3758-4591","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xidian University, Xi\u2019an, PR China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Mathematics and Statistics, Xidian University, Xi'an, PR China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054067811","display_name":"Jimin Ye","orcid":"https://orcid.org/0000-0003-0421-2900"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jimin Ye","raw_affiliation_strings":["School of Mathematics and Statistics, Xidian University, Xi\u2019an, PR China","School of Mathematics and Statistics, Xidian University, Xi'an, PR China"],"raw_orcid":"https://orcid.org/0000-0003-0421-2900","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xidian University, Xi\u2019an, PR China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Mathematics and Statistics, Xidian University, Xi'an, PR China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5054067811","https://openalex.org/A5084206886"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.9863,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.7522291,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"53","issue":"2","first_page":"1020","last_page":"1036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9685999751091003,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9674999713897705,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gumbel-distribution","display_name":"Gumbel distribution","score":0.6965351700782776},{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.6254332065582275},{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.595200777053833},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5012538433074951},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.47351717948913574},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.46695858240127563},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43207859992980957},{"id":"https://openalex.org/keywords/asymptotic-distribution","display_name":"Asymptotic distribution","score":0.4274912476539612},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.42585426568984985},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4167526066303253},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3636040687561035},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"Extreme value theory","score":0.3528459072113037},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.12940317392349243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.10509026050567627}],"concepts":[{"id":"https://openalex.org/C137610916","wikidata":"https://www.wikidata.org/wiki/Q1096862","display_name":"Gumbel distribution","level":3,"score":0.6965351700782776},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.6254332065582275},{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.595200777053833},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5012538433074951},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.47351717948913574},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.46695858240127563},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43207859992980957},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.4274912476539612},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.42585426568984985},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4167526066303253},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3636040687561035},{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.3528459072113037},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.12940317392349243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.10509026050567627},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2022.2039199","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2039199","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12","score":0.46000000834465027}],"awards":[{"id":"https://openalex.org/G2771251299","display_name":null,"funder_award_id":"61772020","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W562988351","https://openalex.org/W571112424","https://openalex.org/W1971846058","https://openalex.org/W2003549425","https://openalex.org/W2010873641","https://openalex.org/W2012200333","https://openalex.org/W2025210383","https://openalex.org/W2033370164","https://openalex.org/W2042087639","https://openalex.org/W2043415670","https://openalex.org/W2062628133","https://openalex.org/W2062832726","https://openalex.org/W2078185468","https://openalex.org/W2110080621","https://openalex.org/W2124687376","https://openalex.org/W2126906689","https://openalex.org/W2131577238","https://openalex.org/W2171365244","https://openalex.org/W2177452242","https://openalex.org/W2298444823","https://openalex.org/W2343132017","https://openalex.org/W2693159830","https://openalex.org/W2773595699","https://openalex.org/W2803193817","https://openalex.org/W2804665992","https://openalex.org/W2805188992","https://openalex.org/W2884849372","https://openalex.org/W2911986121","https://openalex.org/W2940956789","https://openalex.org/W2967534259","https://openalex.org/W2981413487","https://openalex.org/W4206149930","https://openalex.org/W4214752017","https://openalex.org/W4233921518","https://openalex.org/W4242614154","https://openalex.org/W6678119076"],"related_works":["https://openalex.org/W2293264460","https://openalex.org/W2750430821","https://openalex.org/W2736068589","https://openalex.org/W2950895695","https://openalex.org/W3009271360","https://openalex.org/W2167010909","https://openalex.org/W4387723075","https://openalex.org/W4210771670","https://openalex.org/W2065711427","https://openalex.org/W1491652911"],"abstract_inverted_index":{"In":[0,31],"the":[1,13,36,69,77,88,95,101,104,112,132,139,147,157],"reliability":[2,105,144],"analysis":[3,154],"of":[4,52,72,94,103,111,142],"competing":[5,54],"risks":[6,55],"model,":[7,41,96],"it":[8],"is":[9,62,74,84],"usually":[10],"assumed":[11],"that":[12,131,156],"components":[14,73],"are":[15,20,115],"independent":[16,148],"and":[17,68,91,122,161],"failure":[18,38],"causes":[19],"observable.":[21],"However,":[22],"this":[23,32],"assumption":[24],"may":[25],"be":[26],"unrealistic":[27],"in":[28],"some":[29],"applications.":[30],"article,":[33],"based":[34],"on":[35],"tampered":[37],"rate":[39],"(TFR)":[40],"we":[42],"introduce":[43],"a":[44,151],"constant-stress":[45],"partially":[46],"accelerated":[47],"life":[48,70],"test":[49],"(PALT)":[50],"model":[51,113,134,160],"dependent":[53,60,89,133],"with":[56,135,146],"masked":[57,136],"data.":[58],"The":[59,80],"structure":[61],"described":[63,75],"by":[64,76],"Gumbel":[65],"copula":[66],"function":[67,106],"distribution":[71],"Weibull":[78],"distribution.":[79],"maximum":[81],"likelihood":[82],"method":[83],"used":[85],"to":[86,99],"estimate":[87],"parameter":[90],"unknown":[92],"parameters":[93,114],"so":[97],"as":[98],"derive":[100],"estimation":[102,140],"(RF).":[107],"Confidence":[108],"intervals":[109],"(CIs)":[110],"also":[116],"obtained":[117],"via":[118],"asymptotic":[119],"normality":[120],"theory":[121],"bias-corrected":[123],"percentile":[124],"bootstrap":[125],"(Boot-BCP)":[126],"technique.":[127],"Simulation":[128],"results":[129],"show":[130],"data":[137,153],"improves":[138],"accuracy":[141],"system":[143],"compared":[145],"model.":[149],"Finally,":[150],"real":[152],"indicates":[155],"statistical":[158],"inference":[159],"methods":[162],"have":[163],"good":[164],"performance.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}